10x Xenium
Human zone of skin
GSM9357158
silver Interactive viewer systemic sclerosis ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_focus': DataTree[cyx] (4, 17095, 14301), (4, 8547, 7150), (4, 4273, 3575), (4, 2136, 1787), (4, 1068, 893)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (17095, 14301), (8547, 7150), (4273, 3575), (2136, 1787), (1068, 893)
│ └── 'nucleus_labels': DataTree[yx] (17095, 14301), (8547, 7150), (4273, 3575), (2136, 1787), (1068, 893)
├── Points
│ └── 'transcripts': DataFrame with shape: (3365819, 13) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (6953, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (6953, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (6815, 2) (2D shapes)
└── Tables
└── 'table': AnnData (6953, 5101)
with coordinate systems:
▸ 'global', with elements:
morphology_focus (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("7sKWMoHCM6kGAlax0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) Python · lamindb
Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("7sKWMoHCM6kGAlax") # stem uid: Lamin resolves it to the latest version
sdata = artifact.load() # cache, then load into memory
group = artifact.open() # stream without downloading
path = artifact.cache() # sync into the local cache, returns the path Shell
# sddb client: download the .zarr into data/
sddb-cli download 7sKWMoHCM6kGAlax0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/7sKWMoHCM6kGAlax
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 7sKWMoHCM6kGAlaxLicense: NCBI GEO policy
Part of collection
Spatial analyses of early, untreated SSc skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling→ 10 datasets · other
Metadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- zone of skin
- Condition
- systemic sclerosis
- Modality
- RNA
- Observations (cells)
- 6,953
- Transcripts
- 3,365,819
- Tier
- silver
- Chemistry
- Xenium Prime
- Size
- 969 MB
- Sample ID
- GSM9357158
- License
- NCBI GEO policy
- Published
- Dec 12, 2025
Cite this dataset
doi.org/10.1172/jci.insight.198954 ↗ BibTeX
@article{Jarnagin_2025,
title = {Multimodal analyses of early, untreated systemic sclerosis skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling},
volume = {11},
ISSN = {2379-3708},
url = {http://dx.doi.org/10.1172/jci.insight.198954},
DOI = {10.1172/jci.insight.198954},
number = {3},
journal = {JCI Insight},
publisher = {American Society for Clinical Investigation},
author = {Jarnagin, Helen C. and Parvizi, Rezvan and Gong, Zhiyun and Gedert, Rosemary and Xing, Xianying and Tsoi, Lam (Alex) C. and Bogle, Rachael and Morrisson, Madeline J. and Perreard, Laurent and Pioli, Patricia A. and Kolling, Fred and Gudjonsson, Johann E. and Khanna, Dinesh and Whitfield, Michael L.},
year = {2025},
month = Dec
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB